Associations of ABHD2 Genetic Variations with Risks for Chronic Obstructive Pulmonary Disease in a Chinese Han Population

RESEARCH ARTICLE Associations of ABHD2 Genetic Variations with Risks for Chronic Obstructive Pulmonary Disease in a Chinese Han Population

Li Liu1☯, Xiangshun Li2☯, Rui Yuan1, Honghong Zhang1, Lixia Qiang2, Jingling Shen1*, Shoude Jin2*

1 Department of Histology and Embryology, Harbin Medical University, Harbin, Heilongjiang Province, 150018, China, 2 Division of Respiratory Disease, The Fourth Hospital of Harbin Medical University, Harbin, Harbin, Heilongjiang Province, 150001, China

☯ These authors contributed equally to this work. * [email protected] (SJ); [email protected] (JS)

Abstract

The human α/β hydrolase domain-containing protein 2 gene (ABHD2) plays a critical role in OPEN ACCESS pulmonary emphysema, a major subset of the clinical entity known as chronic obstructive ABHD2 Citation: Liu L, Li X, Yuan R, Zhang H, Qiang L, pulmonary disease (COPD). Here, we evaluated genetic variation in the gene in a Shen J, et al. (2015) Associations of ABHD2 Genetic Chinese Han population of 286 COPD patients and 326 control subjects. The rs12442260 Variations with Risks for Chronic Obstructive CT/CC genotype was associated with COPD (P < 0.001) under a dominant model. In the Pulmonary Disease in a Chinese Han Population. former-smoker group, the rs12442260 TT genotype was associated with a decreased PLoS ONE 10(4): e0123929. doi:10.1371/journal. pone.0123929 risk of developing COPD after adjusting for age, gender and pack-years (P = 0.012). Rs12442260 was also associated with pre-FEV1 (the predicted bronchodilator forced expi- Academic Editor: Chunxue Bai, Zhongshan Hospital Fudan University, CHINA ratory volume in the first second) in controls (P = 0.027), but with FEV1/ forced vital capacity (FVC) ratios only in COPD patients (P = 0.012) under a dominant model. Results from the Received: August 16, 2014 current study suggest that ABHD2 gene polymorphisms contribute to COPD susceptibility Accepted: March 9, 2015 in the Chinese Han population. Published: April 16, 2015

Copyright: © 2015 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any Introduction medium, provided the original author and source are credited. Chronic obstructive pulmonary disease (COPD), including chronic pulmonary emphysema and chronic bronchitis, is one of the leading causes of morbidity and mortality worldwide. It is Data Availability Statement: All relevant data are within the paper and its Supporting Information files. characterized by a partially reversible airflow limitation, which is usually progressive and associated with abnormal inflammatory responses of the lungs when exposed to noxious particles Funding: This study was funded by the National or gases [1–4]. According to the World Health Organization, by 2020, COPD may become the Natural Science Foundation of China (30900413), Science Foundation of Heilongjiang Province of fourth most common single cause of death and is expected to be the third-leading cause of China (LC201024), the National Basic Science death in developed countries [5–8]. The global incidence of COPD is approximately 9〜10% in Research Program of China (973 Program) (grant no. adults aged 40 years of age [9], and 8.2% in this age group in China [10]. Accounting for 80– 2012CBA01303). 90% of COPD cases, cigarette smoking is by far the major risk factor for COPD. However, only Competing Interests: The authors have declared 10〜15% of smokers develop clinically significant COPD, and some former smokers and non- that no competing interests exist. smokers can develop COPD [11–14]. Current evidence suggests that inherited risks influence

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 1/11 ABHD2 Genetic Variation and the Risk of COPD

COPD susceptibility, as observed with various cancers and other diseases types. Results from family studies, case-control studies, and recent hypothesis-free genome-wide association (GWA) studies have highlighted the role of genetic factors in COPD development [15, 16], although the molecular genetics of underlying COPD pathogenesis are not completely under- stood. At present, many studies suggest that COPD, a complex polygenic disease, is caused by interaction between genetic and environmental factors. Thus, the identification of genetic and environmental risk factors that cause an increased risk of COPD may be helpful in improving its treatment. The α/β hydrolase domain-containing protein 2 gene (ABHD2, MIM612196) is located at 15q26.1 and is a member of the α/β hydrolase superfamily that was identified in a genetic screen of human emphysematous tissue [17]. ABHD2 encodes a protein containing an alpha/ beta hydrolase fold, which serves a catalytic domain in a wide range of enzymes. Thus, human ABHD2 should have multiple functions in maintaining the structural integrity of lungs, as observed with mouse Abhd2 [18]. Results from previous study revealed that the mouse Abhd2 is expressed in vascular smooth muscle cells (SMCs) and that enhanced migration occurs with cultured vascular SMCs from Abhd2-deficient mice [19]. Enhanced neointimal hyperplasia was observed in Abhd2-deficient mice, using an experimental vascular cuff placement injury model [20]. Previously, we used Abhd2-deficient mice obtained by gene trap mutagenesis to examine the role(s) of Abhd2 in mouse lungs. Our results showed that derangement of alveolar phospholipid metabolism could induce emphysema and that Abhd2 played a critical role in maintaining lung structural integrity [18]. Recently, Tsuyoshi Yoshida et al.[21] reported an association between the human ABHD2 gene and colorectal cancer, although the molecular function of ABHD2 in driving colorectal cancer remains uncertain. Maryam Shahdoust et al.[22] recruited 13 normal smokers and 9 non-smokers to identify differentially expressed genes between 2 groups and assessed the effects of cigarette smoking on large airway epithelia cells. Their results showed that ABHD2 was more highly expressed in smokers, compared with a control group consisting of non- smokers. The aim of the present study was to investigate whether single-nucleotide polymorphisms (SNPs) in the ABHD2 gene are related to COPD in the Chinese Han population using a large panel of samples obtained from the patients with COPD and control subjects.

Results A total of 286 patients with COPD and 326 normal controls were enrolled in this study. The characteristics of the participants enrolled in this study are summarized in Table 1. The mean age was 67.60 years (±9.59 years) for patients with COPD and 66.40 years (±9.76) for control subjects. Males comprised 65.7% of the COPD group, compared with 64.1% in the control groups. Thus, there were no significant differences in the age distribution (P = 0.137) or gender (P = 0.675) between the COPD and control groups. Patients with COPD had greater average smoking exposure (32.80 vs. 25.57 pack-years, P < 0.001), and significantly worsened lung functions than control subjects, in term of the predicted FEV1 percent (69.32% vs. 104.61%, P < 0.001) and FEV1/FVC ratio (0.58 vs. 0.84, P < 0.001). The relative frequencies of ABHD2 SNP polymorphisms observed in patients enrolled in this study are shown in Table 2. The overall call rates for each SNP exceeded 97%. The distribution of genotypes in the control group was consistent with the Hardy-Weinberg equilibrium (HWE; P > 0.05). No significant differences SNP allele frequencies were observed between patients with COPD and control subjects, except for rs12442260 (P = 0.001). In addition, the genotype frequencies between the COPD and controls groups for 6 SNPs were analyzed. A

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 2/11 ABHD2 Genetic Variation and the Risk of COPD

Table 1. Characteristics of COPD patients and healthy controls.

Variable Case subjects(n = 286) Control subjects(n = 326) P Value Mean age (years)a 67.60±9.59 66.40±9.76 0.137b Male % (n) 65.7 (188) 64.1(209) 0.675c Current smoking status Non-smoker % (n) 35.0 (100) 49.1(160) Former smoker % (n) 30.4 (87) 24.5 (80) 0.002c Current smoker % (n) 34.6 (99) 26.4 (86) Pack-yearsa 32.80±10.77 25.57±8.90 <0.001b FEV1 percentage of predicteda, % 69.32±9.14 104.61±7.07 <0.001b FEV1/FVCa, L 0.58±0.07 0.84±0.04 <0.001b

ND = not done; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity. aMean (±SD). bP value was calculated using the t test. cP value was calculated using the χ2 test. doi:10.1371/journal.pone.0123929.t001

statistically significant association was found only between the rs12442260 polymorphism and the risk for developing COPD. Compared to TT homozygotes at the rs12442260 locus, CT het- erozygotes showed a significantly increased risk for COPD (OR = 1.84, 95% CI = 1.31–2.59; P < P < – P 0.001, adjust 0.001), as did CC homozygotes (OR = 1.78, 95% CI = 1.04 3.06; = 0.035, P adjust = 0.025). The rs12442260 genotype exhibited significance under a dominant genetic model (CC + CT vs. TT: OR = 1.83, 95% CI = 1.32–2.53; P < 0.001), indicating that carriers of the C allele had an 83% increased risk for developing COPD compared with homozygotes. However, such an elevated risk was not observed, under a recessive genetic model (CC vs. CT + TT: OR = 1.32, 95% CI = 0.79–2.21; P = 0.282). In addition, individuals with the rs293377 CG genotype had a 43% increased risk for developing COPD compared with homozygotes, as determined by comparative analyses conducted with or without adjust for age, gender, and cur- P rent smoking status and all patients in the COPD and control groups (GC vs. CC: adjust = 0.018). The frequency of the GC allele was increased in patients with COPD by 61.5%, compared to control subjects. No significant differences were observed between the COPD and control groups with respect to the other SNPs. Correlations between COPD and smoking status are well documented. A smoking status stratification analysis was performed to eliminate potential confounding effects caused by differences in smoking history. As shown in Table 3, we found that none of the SNPs studied were significantly associated with COPD in non-smokers (n = 260) or current smokers (n = 185). However, SNP analysis with samples from former smokers (n = 167) showed that the rs12442260 polymorphism was significantly associated with COPD after adjusting for age, – P gender and pack-years (CT vs. TT: ORadjust = 0.401, 95% CIadjust = 0.20 0.82; adjust = 0.012), and the TT genotype was associated with a decreased risk for COPD. Under the assumption of a dominant model of inheritance, there was an association between the rs12442260 polymorphism and COPD (CC + CT vs. TT: OR = 2.07, 95% CI = 1.10–3.89; P = 0.022). Association between haplotypes and diseases can, in some instance, highlight genetic influences that are not detected by SNP analysis. Linkage disequilibrium (LD) values among the 6 SNPs investigated were low, indicating that none of these SNPs was associated with each other (R2 < 0.005). Thus, haplotypes could not be tested in the current study. To assess whether ABHD2 polymorphisms were associated with pulmonary function in the Chinese Han population, a dominant model of genetic association analysis was performed for

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 3/11 ABHD2 Genetic Variation and the Risk of COPD

Table 2. Risk associated with the six polymorphisms.

SNP Genotype / Allele Case n = 286 (%) Control n = 326 (%) Call rate (%) P-HWE ORb (95% CI) P value Pa rs293379 T 205 (36.6) 254 (39.2) 98.7 0.265 0.355 C 355 (63.4) 394 (60.8) TT 31 (11.1) 45 (13.9) 0.75 (0.44–1.26) 0.279 0.273 CT 143 (51.1) 164 (50.6) 0.95 (0.67–1.34) 0.619 0.854 CC 106 (37.8) 115 (35.5) 1 rs293377 G 272 (48.1) 298(46.6) 98.5 0.152 0.604 C 294 (51.9) 342 (51.9) GG 49 (17.3) 63 (19.7) 1.10 (0.67–1.81) 0.703 0.857 GC 174 (61.5) 172 (53.7) 1.43 (0.97–2.12) 0.071 0.018 CC 60 (21.2) 85 (26.6) 1 rs16942690 G 172 (31.0) 211 (33.2) 97.2 0.800 0.433 A 382 (69.0) 425 (66.8) GG 23 (8.3) 36 (11.3) 0.71 (0.40–1.27) 0.249 0.230 GA 126 (45.5) 139 (43.7) 0.91 (0.71–1.61) 0.942 0.801 AA 128 (46.2) 143 (45.0) 1 rs293381 T 225 (39.5) 267 (41.5) 99.2 0.055 0.482 C 345 (60.5) 377 (58.5) TT 28 (9.8) 47 (14.6) 0.69 (0.40–1.19) 0.184 0.184 CT 169 (59.3) 173 (53.7) 1.13 (0.79–1.62) 0.493 0.505 CC 88 (30.9) 102 (31.7) 1 0.182 rs12442260 C 210 (37.0) 182 (28.2) 99.2 0.140 0.001 T 358 (63.0) 464 (71.8) CC 35 (12.3) 31 (9.6) 1.78 (1.04–3.06) 0.035 0.025 CT 140 (49.3) 120 (37.2) 1.84 (1.31–2.59) <0.001 <0.001 TT 109 (38.4) 172 (53.2) 1 rs729707 G 231 (41.4) 289 (44.5) 98.7 0.104 0.284 A 327 (58.6) 361 (55.5) GG 31 (11.1) 57 (17.5) 0.64 (0.38–1.09) 0.098 0.144 AG 169 (60.6) 175 (53.9) 1.14 (0.79–1.64) 0.493 0.513 AA 79 (28.3) 93 (28.6) 1

SNP, single nucleotide polymorphism; HWE, Hardy-Weinberg equilibrium; OR, odds ratio; CI, conﬁdence interval. a Adjusted for age, gender, and smoking status. b Odds ratios are relative to the major homozygous genotype. doi:10.1371/journal.pone.0123929.t002

pre-FEV1 values and FEV1/FVC ratios using general linear models. Individual with the rs12442260 CT/CC genotype had a lower annual average in pre-FEV1 values than did those with the TT genotypes in 323 control subjects (TT: n = 172, 105.44±6.41, CT+CC: n = 151, 103.72±7.59; P = 0.027; Fig 1A), but this decline was not observed among 284 patients with COPD (TT: n = 109, 70.67±8.82, CT+CC: n = 175, 68.54±9.28; P = 0.071). When the analysis was conducted with the COPD group only, the results showed that the rs12442260 locus was associated with FEV1/FVC ratios in the COPD group (P = 0.012; Fig 1B), but not in the control group (P = 0.344). No significant association was evident between the other SNPs and indexes of pulmonary function (pre-FEV1 values and FEV1/FVC ratio).

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 4/11 ABHD2 Genetic Variation and the Risk of COPD

Table 3. Association between SNPs in the ABHD2 and COPD by smoking status-stratified analysis.

Reference Genotypes Non-smokers (n = 260) Former smokers (n = 167) Current smokers (n = 185)

SNP ID COPD Control Pa COPD Control Pb COPD Control Pb n = 100(%) n = 160(%) n = 87(%) n = 80(%) n = 99(%) n = 86(%) rs293379 TT 10 (10.0) 19 (11.8) 0.504 9 (11.1) 12 (15.4) 0.608 12 (12.1) 14 (16.3) 0.530 CT 51 (51.0) 87 (54.4) 0.390 39 (48.2) 38 (48.7) 0.820 53 (53.5) 39 (45.3) 0.306 CC 39 (39.0) 54 (33.4) 33 (40.7) 28 (35.9) 34 (34.3) 33 (38.4) rs293377 GG 14 (14.0) 28 (17.5) 0.954 16 (19.1) 18 (24.3) 0.938 19 (19.2) 17 (19.8) 0.666 CG 70 (70.0) 98 (61.3) 0.196 48 (57.1) 35 (47.3) 0.157 56 (56.6) 39 (45.3) 0.150 CC 16 (16.0) 34 (21.2) 20 (23.8) 21 (28.4) 24 (24.2) 30 (34.9) rs16942690 GG 9 (9.0) 13 (8.3) 0.981 7 (8.4) 10 (12.8) 0.367 7 (7.5) 13 (15.6) 0.296 AG 38 (38.0) 70 (44.6) 0.388 42 (50.6) 32 (41.0) 0.300 46 (48.9) 37 (44.6) 0.994 AA 53 (53.0) 74 (47.1) 34 (41.0) 36 (46.2) 41 (43.6) 33 (49.8) rs293381 TT 11 (11.0) 24 (15.0) 0.716 9 (10.5) 11 (14.5) 0.353 8 (8.1) 12 (14.0) 0.690 CT 61 (61.0) 86 (53.8) 0.369 46 (53.5) 41 (54.0) 0.531 62 (62.6) 46 (53.5) 0.061 CC 28 (28.0) 50 (31.2) 31 (36.0) 24 (31.5) 29 (29.3) 28 (32.5) rs12442260 CC 10 (10.0) 11 (7.0) 0.226 15 (17.4) 14 (17.5) 0.312 10 (10.2) 6 (7.0) 0.173 CT 48 (48.0) 65 (41.4) 0.280 43 (50.0) 26 (32.5) 0.012 49 (50.0) 29 (33.7) 0.058 TT 42 (42.0) 82 (51.6) 28 (32.6) 40 (50.0) 39 (39.8) 51 (59.3) rs729707 AA 27 (27.0) 43 (26.9) 0.176 23 (28.8) 23 (29.1) 0.686 29 (29.3) 27 (31.4) 0.244 AG 61 (61.0) 86 (53.7) 0.765 49 (61.2) 46 (58.2) 0.862 59 (59.6) 43 (50.0) 0.872 GG 12 (12.0) 31 (19.4) 8 (10.0) 10 (12.7) 11 (11.1) 16 (18.6)

SNP, single-nucleotide polymorphism. a P-values adjusted by logistic regression for age, gender in non-smokers. b P-values adjusted by logistic regression for age, gender and pack-years in former and current smokers. doi:10.1371/journal.pone.0123929.t003

Discussion Although cigarette smoking is considered to be an underlying risk factor for developing COPD, only 10 ~15% of chronic, heavy cigarette smokers develop symptomatic airflow obstruction [23]. As a complex disease with genetic-environmental interactions, COPD is one of the primary causes of morbidity and has become the fourth most common single cause of death [24–26]. Findings from GWAS studies, familial studies, and case-control studies, suggest

Fig 1. The association between ABHD2 polymorphism rs12442260 and pulmonary function under a dominant model. (A) Rs12442260 is associated with pre-FVE1 in controls; (B) Rs12442260 is associated with FEV1/FVC ratio in patients with COPD. doi:10.1371/journal.pone.0123929.g001

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 5/11 ABHD2 Genetic Variation and the Risk of COPD

that COPD susceptibility may be influenced by genetic factors [27, 28]. In the current study, we investigated the prevalence of ABHD2 gene variants in 286 Chinese Han patients with COPD and 326 normal controls. Six SNPs, including rs293379, rs293377, rs16942690, rs293381, rs12442260, and rs729707, with minor allele frequencies greater than 10% and pair- 2 wise R > 0.8 values were chosen. To the best of our knowledge, this is the first study to implicate ABHD2 SNPs risk factors for developing COPD. Results from previous studies have clearly demonstrated that ABHD2 specifically expressed in alveolar type II cells [18] and SMCs [29]. Abhd2-deficient mice gener- ated via gene trap insertional mutagenesis developed spontaneous, gradual emphysema. In addition, Abhd2 deletions caused increases in SMC migration and intimal hyperplasia as well as changes in physiological functions caused by cuff placement, which are characteristic symp- toms of COPD development in mouse models. Our results revealed that ABHD2 SNP at the rs12442260 locus that influence COPD risk in the Chinese Han population. Carriers of the C allele had an 83% increased risk of developing COPD compared with homozygotes, under a dominant genetic model (CC + CT vs. TT: OR = 1.83, 95% CI = 1.32–2.53; P < 0.001). In former smokers (n = 167), the rs12442260 polymorphism was significantly associated with COPD under the assumption of a dominant model of inheritance (CC + CT vs. TT: OR = 2.07, 95% CI = 1.10–3.89; P = 0.022). These results indi- cate that polymorphisms in ABHD2 may contribute to the development of COPD in the Chi- nese Han population, and this association may be mediated by smoking behavior, although our data failed to identify this association in current smokers. However, differences in average smoking exposure (i.e. pack-years per subject) may be an important factor to explain this con- flict. Rs293377 was also associated with COPD after adjusting for age, gender, and current P smoking status (GC vs. CC: adjust = 0.018), but no correlation was found when comparing other genotypes (G-allele vs. C or GG vs. CC). A limitation of the study was that only one SNP was identified that was associated with COPD risk. In addition, this study did not provide evidence for associations between ABHD2 haplotypes and the risk of COPD. In a previous study, we provided evidence that Abhd2 plays a critical role in maintaining lung structural integrity, providing a rationale for assessing whether ABHD2 polymorphisms influence pulmonary function in the Chinese Han population in this study. Using a dominant model, only one of the SNPs studied was associated with pulmonary function. The Rs12442260 locus was associated with pre-FEV1 values in control subjects, but not in patients with COPD. This result also suggested that the risk of individuals in the control group with Rs12442260 polymorphisms for developing COPD may be affected by environmental factors. The Rs12442260 locus was also associated with FEV1/FVC ratios, but only in patients with COPD. No significant association was found between the other SNPs and the indexes of pulmonary function (pre-FEV1 values and FEV1/FVC ratios, data not shown). Another limitation of this study is that it is unclear whether rs12442260T variant genotypes affect the normal cellular function of ABHD2. Because the rs12442260 site is located within intron 5 of the ABHD2 gene, it potentially may affect ABHD2 mRNA splicing. An intron-located SNP (rs588076 SNP of the PICALM gene) has been shown to affect gene transcription and splicing [30], supporting the possibility that intronic rs12442260 SNPs may regulate ABHD2 mRNA splicing. Further studies will be needed to study the intronic regions containing regulatory elements, such as en- hancer or attenuator elements, which potentially regulate ABHD2 gene transcription. Investi- gations into the abilities of regulatory genetic markers to predict pulmonary function risks in patients with COPD are essential. In addition to the limitations mentioned above, this study had several strengths. This study is the first to show that ABHD2 polymorphisms are associated with COPD risk in a case-controlled manner. Using logistic regression analysis may delineate the influences of critical

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 6/11 ABHD2 Genetic Variation and the Risk of COPD

environmental and genetic factors. Furthermore, our sample size provided 70.0% power in de- tecting relative genetic risks. However, some limitations were inherent in this case-controlled study and should be noted. The sample size (286 patients with COPD and 326 control patients) was not comparatively large among most other COPD association studies published to date. This study was a hospital-based case-controlled study, meaning that there was the potential for information or selection bias. However, such bias was unlikely to be of significance because the controls were age- and sex-frequency matched with the COPD group. In addition, there is a potential concern that population admixture occurred, which could cause inflated type-I er- rors. To avoid this limitation, patients with COPD and control subjects were evaluated in the same hospital, and the ethnicity was limited to Han Chinese We selected target SNPs in Hap- Map with MAFs higher than 10% in CHB individuals to assess whether the ABHD2 gene was associated with COPD. In this study, smoker-status was the only environmental factor analyzed. Data from previous studies have suggested that COPD is a complex polygenic disease that is caused by the interaction of genetic and environmental factors. Further studies must consider other environment factors such as, for example, aerial pollution. In conclusion, we first performed a comprehensive analysis of SNPs in the ABHD2 gene in a Chinese Han population. Our data revealed that the rs12442260 variant in the ABHD2 gene and its interaction with former smoking status were associated with COPD risk and may be important for pre-FEV1 and the FEV1/FVC ratios in the general population. The results of our study could help define the role of this polymorphism as a risk factor for developing of COPD.

Methods Study population A total of 286 patients with COPD and 326 normal control patients were enrolled between June 2011 and April 2013 at the Division of Respiratory Disease in the Fourth Hospital of Har- bin Medical University of Harbin, China. Control patients were randomly selected from a health check-up program, had normal lung function, and were age- and gender-frequency matched with COPD cases. None of the subjects shared kinship with each other, nor were they related to the Han Chinese ethnic group from the Heilongjiang province in northeast China. As recommended in the Global Initiative for Chronic Obstructive Lung Disease Criteria Guide- lines, patients with COPD were included in this study using the following criteria: age 40 years, a post-bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio < 0.70, an FEV1 of < 80% of the predicted value, and no other respiratory diseases [8]. All participants donated 5 ml peripheral blood after providing written, informed consent to participate in this study. This study was approved by the Ethics Committee of the Fourth Hospital of Harbin Medical University and was conducted according to the Declaration of Hel- sinki Principles.

SNP selection and genotyping analysis Genomic DNA was extracted from whole blood samples using the Gentra Puregene DNA Ex- traction Kit (QIAGEN), according to the manufacturer’s instructions. SNPs were selected using Haploview Tagger [31] and based on CHB data using the following criteria: MAF > 10% and pairwise R2 > 0.8 (Fig 2). Linkage disequilibrium blocks were determined using data from HapMap data release #24 (November 2008; NCBI B36 assembly; dbSNP b126). Six SNPs (rs293379, rs293377, rs16942690, rs293381, rs12442260, and rs729707) located on 15q26.1 were chosen for the ABHD2 gene (S1 Table and S2 Table). SNP genotyping was performed by the sequence-specific priming (PCR-SSP) method. The primer sequences and amplified

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 7/11 ABHD2 Genetic Variation and the Risk of COPD

Fig 2. Haplotype block and LD structure for SNPs in Chr15: 89631381–89745591 in Chinese Han Beijing population. doi:10.1371/journal.pone.0123929.g002

fragment lengths are listed in S3 Table. DNA samples were sequenced using a 3730 DNA Ana- lyzer (Applied Biosystems Inc., USA), as shown in S1 Fig.

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 8/11 ABHD2 Genetic Variation and the Risk of COPD

Statistical analysis Gene allele frequencies were measured in samples from patients with COPD and control patients. Continuous data were presented as the mean ± standard deviation (SD). Differences in demographic characteristics and selected variables observed between patients with COPD and controls were evaluated using the χ2 test (for categorical variables) and the Student’s t-test (for continuous variables). The Hardy-Weinberg equilibrium (HWE) for the control groups was assessed using the chi-square goodness of fit. Under the unconditional logistic regression statistic model with adjustments for age, gender, pack-years, and smoking status, the associations between genotypes and COPD risk were estimated by calculating genotype-specific odds ratios (ORs) and 95% confidence intervals (CIs). We also performed a smoking status-stratified analysis to eliminate the potential impact of cigarettes. In addition, linear regression was performed to assess relationships between SNPs and quantitative phenotypes (pulmonary function). Sta- tistical manipulations were undertaken using the SPSS statistical analysis program, Version 19. P-values less than 0.05 were considered statistically significant for all analyses. To estimate linkage disequilibrium (LD) between pairs of loci in the patient and control populations, a standardized disequilibrium coefficient (D’) and squared correlation coefficient (R2) were calculated using Haploview 4.2 (www.broad.mit.edu/mpg/haploview/)[32]. LD blocks were de- fined in accordance with Gabriel’s criteria [33]. The statistical powers of the study were calculated under the assumption of a small effect size (0.1) with an alpha level of significance of 0.05 [34].

Supporting Information S1 Fig. Genotyping of six SNPs by direct sequencing. (TIF) S1 Table. Raw genotype data of 286 patients with COPD. (XLSX) S2 Table. Raw genotype data of 326 control. (XLSX) S3 Table. Markers genotyped in the current study. (DOC)

Author Contributions Conceived and designed the experiments: SJ JS. Performed the experiments: LL XL. Analyzed the data: LL RY HZ JS. Contributed reagents/materials/analysis tools: HZ LQ. Wrote the paper: LL SJ. Clinical data collection: LL XL LQ.

References 1. Hogg JC, Timens W (2009) The pathology of chronic obstructive pulmonary disease. Annu Rev Pathol 4: 435–459. doi: 10.1146/annurev.pathol.4.110807.092145 PMID: 18954287 2. Lopez AD, Shibuya K, Rao C, Mathers CD, Hansell AL, Held LS, et al. (2006) Chronic obstructive pulmonary disease: current burden and future projections. Eur Respir J 27: 397–412. PMID: 16452599 3. Trajkov D, Mirkovska-Stojkovikj J, Petlichkovski A, Strezova A, Efinska-Mladenovska O, Sandevska E, et al. (2009) Association of cytokine gene polymorphisms with chronic obstructive pulmonary disease in Macedonians. Iran J Allergy Asthma Immunol 8: 31–42. PMID: 19279357 4. Pauwels RA, Rabe KF (2004) Burden and clinical features of chronic obstructive pulmonary disease (COPD). Lancet 364: 613–620. PMID: 15313363

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 9/11 ABHD2 Genetic Variation and the Risk of COPD

5. Pauwels RA, Buist AS, Calverley PM, Jenkins CR, Hurd SS (2001) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease (GOLD) Workshop summary. Am J Respir Crit Care Med 163: 1256–1276. PMID: 11316667 6. Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P, et al. (2007) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 176: 532–555. PMID: 17507545 7. Lokke A, Lange P, Scharling H, Fabricius P, Vestbo J (2006) Developing COPD: a 25 year follow up study of the general population. Thorax 61: 935–939. PMID: 17071833 8. Vestbo J, Hurd SS, Agusti AG, Jones PW, Vogelmeier C, Anzueto A, et al. (2013) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 187: 347–365. doi: 10.1164/rccm.201204-0596PP PMID: 22878278 9. Halbert RJ, Natoli JL, Gano A, Badamgarav E, Buist AS, Mannino DM, et al. (2006) Global burden of COPD: systematic review and meta-analysis. Eur Respir J 28: 523–532. PMID: 16611654 10. Wang X, Li L, Xiao J, Jin C, Huang K, Kang X, et al. (2009) Association of ADAM33 gene polymorphisms with COPD in a northeastern Chinese population. BMC Med Genet 10: 132. doi: 10.1186/ 1471-2350-10-132 PMID: 20003279 11. Rennard SI (2004) Treatment of stable chronic obstructive pulmonary disease. Lancet 364: 791–802. PMID: 15337408 12. Fletcher C, Peto R (1977) The natural history of chronic airflow obstruction. Br Med J 1: 1645–1648. PMID: 871704 13. Sethi JM, Rochester CL (2000) Smoking and chronic obstructive pulmonary disease. Clin Chest Med 21: 67–86, viii. PMID: 10763090 14. Mannino DM, Homa DM, Akinbami LJ, Ford ES, Redd SC (2002) Chronic obstructive pulmonary disease surveillance—United States, 1971–2000. MMWR Surveill Summ 51: 1–16. PMID: 12529087 15. Nakamura H (2011) Genetics of COPD. Allergol Int 60: 253–258. doi: 10.2332/allergolint.11-RAI-0326 PMID: 21778810 16. Castaldi PJ, Cho MH, Cohn M, Langerman F, Moran S, Tarragona N, et al. (2010) The COPD genetic association compendium: a comprehensive online database of COPD genetic associations. Hum Mol Genet 19: 526–534. doi: 10.1093/hmg/ddp519 PMID: 19933216 17. Edgar AJ, Polak JM (2002) Cloning and tissue distribution of three murine alpha/beta hydrolase fold protein cDNAs. Biochem Biophys Res Commun 292: 617–625. PMID: 11922611 18. Miyata K, Oike Y, Hoshii T, Maekawa H, Ogawa H, Suda T, et al. (2005) Increase of smooth muscle cell migration and of intimal hyperplasia in mice lacking the alpha/beta hydrolase domain containing 2 gene. Biochem Biophys Res Commun 329: 296–304. PMID: 15721306 19. Miyata K, Nakayama M, Mizuta S, Hokimoto S, Sugamura K, Oshima S, et al. (2008) Elevated mature macrophage expression of human ABHD2 gene in vulnerable plaque. Biochem Biophys Res Commun 365: 207–213. PMID: 17980156 20. Jin S, Zhao G, Li Z, Nishimoto Y, Isohama Y, Shen J, et al. (2009) Age-related pulmonary emphysema in mice lacking alpha/beta hydrolase domain containing 2 gene. Biochem Biophys Res Commun 380: 419–424. doi: 10.1016/j.bbrc.2009.01.098 PMID: 19250629 21. Yoshida T, Kobayashi T, Itoda M, Muto T, Miyaguchi K, Mogushi K, et al. (2009) Clinical omics analysis of colorectal cancer incorporating copy number aberrations and gene expression data. Cancer Inform 9: 147–161. 22. Shahdoust M, Hajizadeh E, Mozdarani H, Chehrei A (2013) Finding genes discriminating smokers from non-smokers by applying a growing self-organizing clustering method to large airway epithelium cell mi- croarray data. Asian Pac J Cancer Prev 14: 111–116. PMID: 23534707 23. Burchfiel CM, Marcus EB, Curb JD, Maclean CJ, Vollmer WM, Johnson LR, et al. (1995) Effects of smoking and smoking cessation on longitudinal decline in pulmonary function. Am J Respir Crit Care Med 151: 1778–1785. PMID: 7767520 24. Celli BR (2008) Update on the management of COPD. Chest 133: 1451–1462. doi: 10.1378/chest.07- 2061 PMID: 18574288 25. Ito K, Barnes PJ (2009) COPD as a disease of accelerated lung aging. Chest 135: 173–180. doi: 10. 1378/chest.08-1419 PMID: 19136405 26. Anto JM, Vermeire P, Vestbo J, Sunyer J (2001) Epidemiology of chronic obstructive pulmonary disease. Eur Respir J 17: 982–994. PMID: 11488336

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 10 / 11 ABHD2 Genetic Variation and the Risk of COPD

27. Sandford AJ, Silverman EK (2002) Chronic obstructive pulmonary disease. 1: Susceptibility factors for COPD the genotype-environment interaction. Thorax 57: 736–741. PMID: 12149538 28. Silverman EK (2002) Genetic epidemiology of COPD. Chest 121: 1S–6S. PMID: 11958158 29. Chen Y, Guo Y, Ge X, Itoh H, Watanabe A, Fujiwara T, et al. (2006) Elevated expression and potential roles of human Sp5, a member of Sp transcription factor family, in human cancers. Biochem Biophys Res Commun 340: 758–766. PMID: 16380080 30. Parikh I, Medway C, Younkin S, Fardo DW, Estus S (2014) An intronic PICALM polymorphism, rs588076, is associated with allelic expression of a PICALM isoform. Mol Neurodegener 29; 9:32. doi: 10.1186/1750-1326-9-32 PMID: 25169757 31. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21: 263–265. PMID: 15297300 32. Shi YY, He L (2005) SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res 15: 97–98. PMID: 15740637 33. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, et al. (2002) The structure of haplotype blocks in the human genome. Science 296: 2225–2229. PMID: 12029063 34. Faul F, Erdfelder E, Lang AG, Buchner A (2007) G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods 39: 175–191. PMID: 17695343

PLOS ONE | DOI:10.1371/journal.pone.0123929 April 16, 2015 11 / 11